Petrophysical AnalysisWorkflow Process.pdf
- 1. Petrophysical Analysis Workflow Clastics, Carbonates, Clay Volume Methods, and Common Cautions Prepared By Tarek Abdellatif Formation evaluation: Importance, types, tools used, & challenges
- 2. Overview of Petrophysical Workflow 1) Data acquisition and QC 2) Environmental correction 3) Lithology identification 4) Clay volume estimation 5) Porosity evaluation 6) Water saturation calculation 7) Permeability estimation 8) Net Pay determination& Hydrocarbon Typing 9) Core-Log Integration & Validation 10) Final Report & Interpretation
- 3. 1) Data Acquisition & QC Ø Collect well logs (gamma ray, resistivity, neutron, density, sonic, etc.), core data, mud logs, drilling reports, and MDT, etc. Ø Check for depth mismatches, tool malfunctions, and validate log quality. 2) Environmental Corrections Ø Apply corrections for borehole effects, tool standoff, mud invasion, etc. Ø Ensure accurate responses especially for RHOB, NPHI, and resistivity. 3) Lithology Identification Ø Use crossplots (RHOB-NPHI, M-N, etc.) Ø Supplement with core/XRD if necessary. Ø In carbonates, spectral gamma ray (U, Th, K) may help identify clay types.
- 4. 4) Clay Volume Estimation Ø Start with GR-based methods (linear, non-linear). Ø Validate using spectral GR, or neutron-density separation. Aspect Linear Methods Non-Linear Methods Basic Concept Assume a straight-line relationship between log response (usually GR) and Vclay. Use empirical or mathematical models to account for the non- linear response of clays in logs. Common Equations - Linear GR Index: - Larionov (Tertiary/Russian) - Clavier Equation - Steiber Equation Simplicity Very easy to apply, quick estimate. More complex, but generally more accurate in shaly formations. Accuracy Reasonable when shaliness is low and GR responds linearly. Better suited for moderate to high clay content or older formations with radioactive minerals. Limitations - Overestimates Vcl in high-GR formations with radioactive sands or non-clay minerals. - Assumes GR is only due to clay. - Requires choosing the right equation for formation age/type. - May underestimate in some extreme cases. Use Cases Quicklook analysis or clean sands. Detailed analysis, shaly sands, or complex lithologies. min max min log = GR - GR GR - GR cl V
- 5. 5) Porosity Evaluation Ø Clastics: Use density-neutron crossplots (Wyllie or Raymer-Hunt-Gardner equations). Ø Carbonates: Account for dual porosity (matrix + vugs/fractures) using sonic, neutron, and density logs. Calibrate with core where possible.
- 6. 6) Water Saturation Calculation Ø Use Archie for clean formations. Ø Use Simandoux, Dual Water, etc. for shaly sands. Feature Archie Simandoux Dual Water Reservoir Type Clean Sands Shaly Sands Shaly Sands (complex clays) Clay Conductivity Not Considered Explicitly Included Explicitly Modeled Complexity Simple Moderate Complex Data Requirements Basic Logs (RT, PHI) + Vsh, Rsh + Bound Water Salinity Best For Clean formations Moderate shale High shale, mixed clays
- 7. 7) Permeability Estimation Ø Use empirical models like Timur-Coates or Wyllie-Rose. Ø Refine with core or NMR data . 8) Net Pay Calculation & Hydrocarbon Typing Ø Apply cut-offs to PHI, Sw, Vcl to determine net reservoir and net pay. Ø Differentiate oil, gas, and water using resistivity and neutron-density separation. 9) Core-Log Integration & Validation Ø Calibrate log-derived properties with core data (porosity, permeability, saturation). Ø Perform uncertainty analysis. 10)Final Report & Interpretation Ø Generate petrophysical summaries (net pay, hydrocarbon pore volume). Ø Provide recommendations for further perforating and testing (DST, production logging).
- 8. Ø Poor log quality due to washouts, tool failures, or bad hole conditions can mislead interpretation. Ø Calibration with core data is essential to validate log-derived properties like porosity, Sw, and permeability. 1) Data Quality and Calibration 2) Depth Mismatch Ø Always depth-match logs: sometimes there is depth shift within logs(GR,ND) and (especially core depths vs. log depths) to avoid errors in correlating zones. 3) Incorrect Environmental Corrections Ø Logs must be corrected for borehole effects, mud filtrate invasion, and tool standoff— especially in deviated or horizontal wells. 4) Misidentification of Lithology Ø Using default lithology assumptions (e.g., sandstone matrix) in mixed lithologies (carbonates, volcanics) may lead to incorrect porosity and Sw. Ø Use crossplots and core/XRD for accurate mineral identification. Cautions in Petrophysical Analysis
- 9. 5) Inappropriate Water Saturation Models Ø Archie’s equation works effectively in clean formations. Ø Shaly sands require appropriate models (e.g., Simandoux, Waxman-Smits, Dual Water). Ø Wrong assumptions about Rw, m, n, salinity lead to major Sw errors. 6) Assumption of Constant Rw Ø Formation water resistivity (Rw) may vary with depth or fluid type, avoid using constant Rw without verification. 7) Ignoring Reservoir Heterogeneity Ø Petrophysical properties vary laterally and vertically, where single-well analysis may not capture this unless supported by core, seismic, or multi-well analysis. 8) Cut-off Selection Ø Arbitrary cut-offs for porosity, Vclay, Sw, etc., may exclude pay zones or include non-pay. Ø Use sensitivity analysis or calibrate with test and/or production data.
- 10. 9) Over-reliance on Software Defaults Ø Default values in software (e.g., IP, Techlog) can be misleading—always customize parameters based on formation and regional knowledge. 10) Lack of Integration Ø Failing to integrate core data, mud logs, production tests, or seismic interpretation leads to a narrow view of the reservoir.
- 11. Clastic vs. Carbonate Reservoirs In Petrophysics Ø Clastics: Uniform lithology, intergranular porosity, Archie’s model often suitable. Ø Carbonates: Complex pores (vuggy, moldic, fractures), diagenetic effects, need core/NMR support Aspect Clastic Reservoirs Carbonate Reservoirs Lithology Identification GR, SP, RHOB-NPHI crossplots More complex: requires crossplots, PE log, image logs, and sometimes core/XRD. Porosity Estimation RHOB, NPHI, and Sonic logs Complex pore systems (vugs, moldic, intercrystalline) make log-derived porosity less accurate—often requires core calibration. Porosity Types Intergranular porosity; fairly uniform. Multiple pore types: vuggy, moldic, intercrystalline, fractures—requires dual- porosity models. Saturation Models Archie for clean sands; Simandoux or Indonesian for shaly sands Archie can be problematic due to tortuosity and cementation—often needs modified exponents or dual porosity saturation models. Rock Typing GR and log responses. Requires more advanced techniques like SCAL, capillary pressure, and image logs. Permeability Prediction Log-based empirical models (e.g., Coates) Poor correlation between porosity and permeability due to pore complexity—core data or NMR often required. Cut-off Determination PHIe, Vclay, and Sw cut-offs are more standardized. Cut-offs are less predictable, often require core calibration and test data. Diagenesis Impact Less diagenetic variability in many cases. Strong diagenetic overprint (cementation, dissolution) greatly affects petrophysical properties. Fracture Presence Often unfractured or minor influence. Fractures may dominate permeability— image logs or FMI critical. Porosity-Permeability Relationship Often linear or predictable. Highly scattered—requires rock typing and advanced statistical techniques.
- 13. M-N Plot
- 15. Clay Mineral Determination with Spectral Gamma ray Th/K Plot
- 16. K/PEF Plot
- 17. Th/K ratio /PEF Plot
- 18. Computer Processed Interpretation(CPI) Petrophysical analysis of well logs and core samples for reservoir evaluation, Emad A. Abd El Aziz 2022
- 19. Computer Processed Interpretation(CPI) Sequoia reservoir evaluation through conventional petrophysical analysis in Eastern Mediterranean, Hassan M. El Shayeb2019
- 20. Petrophysical properties Destribution Sequoia reservoir evaluation through conventional petrophysical analysis in Eastern Mediterranean, Hassan M. El Shayeb2019